560-03-008 ISMRM Abstract

Achieving Whole-Heart CMR in a Single Breath-Hold with Deep Learning Super-Resolution

Primary: Cardiovascular - Vascular and Vessel Wall

Secondary: Analysis Methods - Image Enhancement

560-03-008 · Congenital Heart Disease, Valves, and Vessels · Wednesday, 13 May, 1:40 PM–2:35 PM · Digital Posters Row A

Keywords: Fast MRI Whole-heart MRI Super Resolution Aortic segmentation Pulmonary artery segmentation

Accepted

Yue Jiang ¹, Mark Wrobel¹, Vivek Muthurangu¹, Jennifer Steeden¹

¹Centre for Translational Cardiovascular Imaging, University College London, London, United Kingdom

Presenting Author: Yue Jiang

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References

1. Steeden JA, Quail M, Gotschy A, Mortensen KH, Hauptmann A, Arridge S, et al. Rapid whole-heart CMR with single volume super-resolution. J Cardiovasc Magn Reson 2020;22:1–13. https://doi.org/10.1186/s12968-020-00651-x. [doi]

2. Zhuang X, Li L, Payer C, Štern D, Urschler M, Heinrich MP, et al. Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge. Med Image Anal 2019;58:101537. https://doi.org/10.1016/j.media.2019.101537. [doi]

3. Pace DF, Contreras HTM, Romanowicz J, Ghelani S, Rahaman I, Zhang Y, et al. HVSMR-2.0: A 3D cardiovascular MR dataset for whole-heart segmentation in congenital heart disease. Sci Data 2024;11:1–11. https://doi.org/10.1038/s41597-024-03469-9. [doi]

4. Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 2021;18:203–11. https://doi.org/10.1038/s41592-020-01008-z. [doi]

5. Wrobel M, Pascale M, Yao T, Campbell R, Milano E, Quail M, et al. High Resolution Isotropic 3D Cine imaging with Automated Segmentation using Concatenated 2D Real-time Imaging and Deep Learning 2025. http://arxiv.org/abs/2506.22532.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/560-03-008